© 2018 Elsevier LtdMissing data from air quality datasets is a common problem, but is much more severe in small cities or localities. This poses a great challenge for environmental epidemiology as high exposures to pollutants ...

This paper presents a proposal based on an evolutionary algorithm to impute missing observations in multivariate data. A genetic algorithm based on the minimization of an error function derived from their covariance matrix ...

In this paper, we document a study that involved applying a multiple imputation technique with chained equations to data drawn from the 2007 iteration of the TIMSS database. More precisely, we imputed missing variables ...

In this paper, we document a study that involved applying a multiple imputation technique with chained equations to data drawn from the 2007 iteration of the TIMSS database. More precisely, we imputed missing variables ...

Imputation methods were developed with the purpose of defining estimates for missing data in a database and, in this way, solving possible problems generated by the loss of such information. In this study the objective ...

© The Author(s) 2021.A key hypothesis in epidemiological studies is that time to disease exposure provides relevant information to be considered in statistical models. However, the initiation time of a particular condition ...